The accurate assignment of energies and forces to atoms of small molecules is essential to many branches of chemistry and material science.[1]} Multiple computational methods have been developed to solve this many-body quantum mechanical problem, such as Density Functional Theory (DFT) [2]} and Coupled Cluster (CC) theory [3]}.
More recently, machine learning methods have been trained on datasets obtained from DFT or CC and are shown to be able to predict energies within errors comparable to the accuracy of DFT.[4]} One well-known algorithm is ANI [5]}, for which different training datasets are available [6]}, [7]} that have been created with support of unsupervised training [7]}. ANI is still being developed and was recently extended [9]} beyond the initial four atom types and is also available as a PyTorch implementation [10]}. The ANI network computes atomic environment vectors (AEV) for each atom in a system and passes the vectors through a dedicated fully connected neural network corresponding to the atom type.
